Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds meaningful behavioral context by stating that means are computed per methodology version and never across methodology changes, which is a non-obvious data behavior beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The core definition is front-loaded, followed by concrete use cases and a key methodological caveat, all in a compact and scannable format.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter read-only tool, the description covers purpose, use cases, and an important data nuance. It does not explicitly describe the output shape, but the absence of an output schema is mitigated by the clear 'daily fixings' framing and the simple parameter set.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema fully documents both parameters. The description does not add parameter-specific details, but it does add useful context about the nature of the returned data; baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as returning daily fixings since the index began, with a specific verb and resource. It also gives concrete use cases ('see whether a rate is unusual, or how the cost of capital has moved'), which distinguishes it from sibling tools like get_rate or compare_rate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: to assess unusual rates or capital cost movement. It does not explicitly name alternatives or exclusion conditions, but the use-case framing provides clear context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.